A Systematic Literature Review of Computational Studies in Aquaponic System

The word aquaponics means the growth of aquatic organisms as well as plants in the controlled environment. As the nutrients used for sustainable plant growth is obtained from aquatic organisms and the nutrients that are absorbed by the plants remediate the water for the aquatic life. The advancement in the computational studies plays a vital role in every field of life. The aim of the proposed study is to deeply analyze the computational studies that used IoT, AI, Machine learning and deep learning for aquaponic systems between the years 2019 to 2022. The literature survey deeply discuss the proposed methodology, comprehends the fundamental researches, tool, advantages, limitations, concepts, and results of the recent studies proposed by the researchers in context of aquaponic system. The proposed study extract 41 research articles from these libraries based on year of publication, title, methodology, citation, paper quality and abstract. These articles are collected from seven different research article libraries including Google Scholar, Worldwide Science, IEEE Xplore, Google Books, Refseek, ACM digital Library and Science Direct. This study develops a state of the art research for the next researchers to work on the loopholes of the previous researches in an efficient manner. The results of the proposed study shows that the implementation of IoT based machine learning and deep learning framework shows state of the art results for the nutrients regulation, sensing, monitoring and controlling of the aquaponic environment. It is concluded from the proposed study that there need to be develop ensemble learning model with an efficient dataset in context of aquaponic environment.

@article{Taji2023,
  title     = {A Systematic Literature Review of Computational Studies in Aquaponic System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Khaoula Taji and Ali Sohail and Yassine Taleb Ahmad and Ilyas Ghanimi and Sheeba Ilyas and Fadoua Ghanimi},
  doi       = {10.14569/IJACSA.2023.0140936},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140936}
}